Do average higher moments predict aggregate returns in emerging stock markets?

PurposeIt has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.Design/methodology/approachTwo measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.FindingsThe authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.Originality/valueThis is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Adaptive Market Hypothesis and Time-varying Contrarian Effect: Evidence From Emerging Stock Markets of South Asia

This study contributes to the growing debate on the relation between varying stock market conditions and the profitability of stock market anomalies. We investigate the effect of changed market conditions on time-varying contrarian profitability in order to examine the presence of the Adaptive Market Hypothesis (AMH) in South Asian emerging stock markets. The empirical findings reveal that a strong contrarian effect holds in all the emerging markets. We also find the stock return opportunities vary over time based on contrarian portfolios. We show that contrarian returns strengthen during the down state of market, higher volatility and crises periods, particularly during the Asian financial crisis. Interestingly, the market state instead of market volatility is the primary predictor of contrarian payoffs, which contradicts the findings of developed markets. We argue that the linkage arises from structural and psychological differences in emerging markets that produce unique intuitions regarding stock market anomalies returns. The overall findings on the time-varying contrarian returns in this study provide partial support to AMH. Another significant outcome of this study implies that investors in South Asian emerging markets, like investors in the developed markets, could not adapt to evolving market conditions. Therefore, contrarian profits often exist, and persistent weak-form market inefficiencies prevail in these markets.

Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets

PurposeThis paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.Design/methodology/approachMany VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).FindingsThe results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.Originality/valueAn important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.

Cross-Hedging Portfolios in Emerging Stock Markets: Evidence for the LATIBEX Index

We consider alternative possibilities for hedging spot positions on the FTSE LATIBEX Index, the index of the only international market exclusively for Latin American firms that is denominated by the euro. Since there is not a futures market on the index, it is unclear whether a relatively successful hedge can be found. We explore the plausibility of employing futures on four stock market indices: EUROSTOXX 50, S&P500, BOVESPA, and IPC, and simulate the results that could be obtained by a hedge position based on either unconditional or conditional second order moments estimated from different asymmetric GARCH models. Several criteria for hedging effectiveness suggest that futures contracts on BOVESPA should be preferred, and that a salient reduction in risk can be achieved over the unhedged LATIBEX portfolio. The evidence in favor of a better performance of conditional moments is very clear, without significant differences among the alternative GARCH specifications.

Stocks Abnormal Returns and Rate of Dividend Announcements

The present research study examined the impact of different dividend rate announcements on stocks prices in the Indian stock market. Stocks selected from S&P BSE 500 index and study period from 2008 – 2017. The sample used for this study is 1755 pure cash dividend announcements (492 large-caps, 425 mid-caps, and 838 small-caps). Dividend rates are classified into six classifications to test the stocks' abnormal returns to different dividend classifications. Event methodology market model used to calculate Average Abnormal Returns (AAR) and Cumulative Average Abnormal Returns (CAAR). The results were observed twenty-one times based on market capitalization and dividend rate wise for a final dividend announcement. The results of the study are not the same for different dividend rate classifications and different market capitalizations. The study found positive abnormal returns on event day in most of the classifications, and it is similar to Litzenberger and Ramaswamy (1982), Asquith and Mullins Jr (1983), Grinblatt, Masulis and Titman (1984), Chen, Nieh, Da Chen, and Tang (2009) and many previous research results studied in major developed stock markets and emerging stock markets. Full sample and small-cap final dividend rate 100 percent to 199 percent average abnormal returns are positively significant, and other final dividend rate classification abnormal returns are positive in most of the observations, but returns are not significant. Large-cap average abnormal returns are more sensitive to different dividend rates, and small-cap reacts positively in all classifications. So, different market capitalization final dividend actions impact on stocks in India varies in different dividend rate classifications.

Good and bad high-frequency volatility spillovers among developed and emerging stock markets

PurposeThis paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns.Design/methodology/approachThis paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility.FindingsThe results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects.Originality/valueThe presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.